| 1 | Development of a PDRA Method for Detection of the D614G Mutation in COVID-19 Virus—Worldwide,2021显示文摘ABSTRACT Background:COVID-19 infection is a major public health problem worldwide,and the D614G mutation enhances the infectivity of COVID-19.Methods:A probe-directed recombinase amplification(PDRA)assay was discussed to detect the D614G mutation at 39℃for 30 min.The sensitivity,specificity,and reproducibility of the PDRA were evaluated by D614 and G614 recombinant plasmids.The clinical performance of PDRA assay was validated by testing of 53 previously confirmed COVID-19 positive RNAs and 10 negative samples.Direct sequencing was carried out in parallel for comparison.Result:With good reproducibility and specificity,the PDRA assay worked well with the concentration in the range of 103–107 copies/reaction.Compared with direct sequencing as a reference,the recombinase-aided amplification(RAA)assay obtained 100%sensitivity and 100%specificity using clinical samples. | Ziwei Chen Xinxin Shen Ji Wang Xiang Zhao Yuan Gao Ruiqin Zhang Jinrong Wang Leping Liu Xinmin Nie Xuejun Ma | 2021 | China CDC weekly2021，3，21： | 1 |
| 2 | Comparison analysis of sampling methods to estimate regional precipitation based on the Kriging interpolation methods: A case of northwestern China显示文摘The accuracy of spatial interpolation of precipitation data is determined by the actual spatial variability of the precipitation, the interpolation method, and the distribution of observatories whose selections are particularly important. In this paper, three spatial sampling programs, including spatial random sampling, spatial stratified sampling, and spatial sandwich sampling, are used to analyze the data from meteorological stations of northwestern China. We compared the accuracy of ordinary Kriging interpolation methods on the basis of the sampling results. The error values of the regional annual precipitation interpolation based on spatial sandwich sampling, including ME(0.1513), RMSE(95.91), ASE(101.84), MSE(-0.0036), and RMSSE(1.0397), were optimal under the premise of abundant prior knowledge. The result of spatial stratified sampling was poor, and spatial random sampling was even worse. Spatial sandwich sampling was the best sampling method, which minimized the error of regional precipitation estimation. It had a higher degree of accuracy compared with the other two methods and a wider scope of application. | JinKui Wu ShiWei Liu LePing Ma Jia Qin JiaXin Zhou Hong Wei | 2016 | Research in Cold and Arid Regions2016，8，6： | 0 |
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